“Machine learning is the process of turning data into a computer program’s decisions…Once the basic algorithm is written, the system learns on its own. The market’s top technology companies are collecting peta-bytes of customer data, satellite images, web site content, weather sensor data, and much more. What’s left to turn all this information into cars that drive themselves, drones that carry out autonomous missions, online shopping platforms that know what you’ll buy before you do and the next step in human evolution that is virtual reality? Massive, massive, processing power. Enter NVIDIA (NASDAQ:NVDA).”

“With Nvidia, we are looking at machine learning, artificial super intelligence, big data, gaming, self-driving cars, virtual reality and a lot more. Nvidia…literally invented the graphics processing unit (GPU) which now serves as the visual cortex of modern computers and artificial intelligence.”

“NVIDIA has its hands in the most innovative segments of technology… Deep learning is the secret weapon for self-driving car algorithms. NVIDIA already calls Tesla, Aston Martin, Rolls Royce and Audi as customers.” The article also mentions other areas with great growth potential for NVIDIA.

Note that I don’t own this stock (but I do have on order a Tesla Model 3 which will likely use Nvidia’s technology.) I only mention this company as an example of how AI is beginning to be integrated into our everyday lives.

There has been some discussion on how AI may never be able to truly “think” any better than a human. That may be. But there seems to be little doubt that given enough devices (sonar; radar; GPS; visual cameras ahead, side, and behind; etc.) and enough computer power and comparable software, that self-driving will eventually be far superior to humans. Note that I did not say perfect. Humans are quite poor at driving, hence the over 5 million car crashes in the US per year and 32,000 deaths. All AI has to do is be substantially better than that.

One of the readers asked me to look at a conversation with Gary Marcus on edge.com. The conversation was so broad that it is hard to comment. But here are a couple of observations. Gary kept comparing AI to humans as if humans are all that good at either decision making or thinking. We are not all that good at either! And Gary kept inferring that the brain is so superior that we may never match it. Well, we may not have to. Box jellyfish have no traditional brains, but they have 24 eyes, 4 of which are quite sophisticated. They are able to do what they have to do in their environment quite well, without a traditional brain. So it will be with AI. Gary noted how auto-drive is only sufficient in very controlled conditions (i.e. highways with well-marked lanes.) They don’t do well in snow, fog, etc. Well, nor do we! But the algorithms being used in auto-drive are self-learning, and they may very well learn that their current sensors along with identifying the location of bridge abutments, light poles, trees, and other cars, and by using advanced GPS, that they can find where they are on the road far more effectively than a human driver in snow, fog, rain, etc.

As for very advanced thinking, we aren’t very good at this unless we have experimental data. After all these years, we still don’t understand the brain. And scientists trying to understand the Big Bang have had to add in an initial expansion that exceeds the speed of light and to incorporate their recent “findings” that the universe is expanding at an ever increasing rate. They have included a fudge factor called black energy. They have no idea what this is, and when Einstein did something similar he later admitted this was the dumbest thing he had ever done!

Despite all that, AI is progressing quite nicely.

As promised regards the stock market, here is an update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that didn’t believe it! The current S&P is 2102, up 11.8% since EW’s prediction early this year.

I haven’t posted for a while because it seems like we are all just watching AI being implemented in many different areas. As expected, the initial areas of implementation have been narrowly focused, but their impacts are already being felt. For example, Tesla Motors has been incorporating their autonomous drive system very rapidly, such that they feel that they can already see substantial gains. To quote their CEO, Elon Musk:

“The probability of having an accident is 50% lower if you have Autopilot on. Even with our first version. So we can see basically what’s the average number of kilometers to an accident – accident defined by airbag deployment. Even with this early version, it’s almost twice as good as a person . . . I think it’s going to be important in terms of satisfying regulators and the public to show statistically with a large amount of data – with billions of kilometers of driving – to say that the safety level is definitively better, by a meaningful margin, if it’s autonomous versus non-autonomous.”

It will be a while before Tesla can log billions of miles of data, but already Tesla drivers have recorded 47 million miles in Autopilot mode. I recently rode in a Tesla with Autopilot, and it truly is remarkable. It not only had automatic braking in case of an obstacle ahead, but had lane maintenance ability. It was easy to see how a human driver, especially if they look away for a second or so, would be slower to respond to an issue in front or next to their car. (Note that I am one of the 400,000 people who put a $1,000 deposit on the Tesla Model 3 that will not be out for over a year. So I may be biased.)

As I have noted in earlier posts, Elon Musk is afraid that AI could be a real risk for mankind as AI gets more and more advanced and powerful. Not everyone agrees. Zuckerberg of Facebook believes that mankind will easily be able to control AI, and he is clear that Facebook will be pushing AI to its limit, including using face recognition systems.

I have been spending some time trying to understand where AI’s strengths and weaknesses will be. AI will certainly surpass man’s ability to use existing data. Thus areas like medicine, accounting, law, investing, and teaching are at risk. And in many areas the strength of AI will come in its growing ability to do calculated guessing. A lot of the gains on vision systems used for autonomous driving come from improvements in software and chip design that enable the computer to make judgments on limited data, much as our brain does.

But AI still requires understanding, measurement, and data. Many of the unknown areas mankind is pursuing may not be enhanced by AI. For example, quantum entanglement, which can include instant communication across long distances and which Einstein called spooky, is still not understood by anyone. Even areas we thought we understood, like our universe starting with the Big Bang has been muddied by including expansion (which had to go faster than the speed of light), and the question whether the expansion happened before or after the Big Bang. Or even the size and age of our universe given all the questions related to red shift that is used in so many measurements. Is our universe expanding (at an ever increasing rate) or contracting? What is dark matter and dark energy?

Even evolution has issues related to Darwin believing everything was gradual whereas some fossils and some tests show that some evolutionary changes are apparently sudden. We are not as smart as we think we are, and in these areas AI may not be any smarter!

As promised, here is an update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that I didn’t believe it! The current S&P is 2065, up almost 10% since EW’s prediction early this year. Are you fans of EW still holding in there?

NYU research psychologist Gary Marcus has said that “virtually everyone” who works in AI believes that machines will eventually overtake us: “The only real difference between enthusiasts and skeptics is a time frame.”

We have seen that Google’s DeepMind system is proficient at “Go;” we have seen the almost daily advances towards autonomous driving; we have seen the apparent use of AI in investment programs that chase the stock market; and we have seen AI medical programs that already can beat doctors in identifying some types of illnesses. But this does NOT mean that true thinking AI is going to happen in the next few years. I believe that the 2025 date I forecast in my novel “Artificial Intelligence Newborn, it is 2025 and I am Here” is still a good estimate.

All the current AI programs are narrow in that they are apparently only searching data already identified as being applicable to the subject of interest. As time goes on and computers become more powerful, the search restraints will be opened to look outside the current search boundaries such that discoveries/correlations will be found that humans have not yet seen. And from these discoveries the computers/software will extend knowledge beyond what we mere humans can. Will this be thinking? If you are religious and think that God gave us special thinking abilities, perhaps you will not accept this as thinking. If you believe that computers must “think” in the same manner as the human brain, then you may not accept computer AI as thinking. But if you only look at the output of AI, and accept that it could well be the next step in evolution, then what will eventually happen with AI will be truly “thinking.”

But that may create a problem. We humans have a lot of hang-ups that cause war, global warming, risk of nuclear disaster, etc. If computers don’t have these emotional hang ups, at what point will the computers identify humans as a problem to be solved? And what options will they consider to solve the “human problem?” Will they only act on this with human approval, or at some point will they recognize that humans are just a hindrance to advanced knowledge.

As I promised, here is an update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that don’t believe it! The current S&P is 2016, up 7.2% since EW’s prediction early this year.

In past updates I suggested that the first applications of artificial intelligence would occur in the stock market, because mutual funds and hedge funds would fund the development of the required AI to beat the market. Of course these investment companies would not make their applications of AI public because that would either discount their value or make the government take action to remove these obvious advantages over the average investor.

But I believe that their trail is obvious if you look at a chart of the S&P 500 for the last five years. You will see that the character of changes in the S&P 500 have changed dramatically in recent years. Both the percent of change and the velocity of change have increased versus earlier periods. You can look at this on either a linear or log chart and you will see the same thing. Note Oct 19, 2014; Aug 16, 2015; Oct 4, 2015; and Jan 4, 2016.

This is not enough data to conclude that this is statistically significant to a 95% confidence level, but it is getting close. And it certainly is worth watching.

As I have said before, I would not try to play the general market with what is going on. Buying a specific stock for its growth potential may be valid. But in my opinion, the overall market is now being largely influenced by AI programs.

A 2/4/2016 article by Cade Metz of Cade Metz Business describes how Artificial Intelligence (AI) is transforming Google Search. Here are some relevant quotes from this article that supports how AI is proceeding on its ability to discover “deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale.”

“The truth is that even the experts don’t completely understand how neural nets work. But they do work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language—words or phrases that people might type into a search engine—it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers.”

“At one point, Google ran a test that pitted its search engineers against Rank Brain,” a deep learning system. “Both were asked to look at various web pages and predict which would rank highest on a Google search results page. RankBrain was right 80 percent of the time. The engineers were right 70 percent of the time.”

“Increasingly, we’re discovering that if we can learn things rather than writing code, we can scale these things much better.”

Those of you who have read my book Artificial Intelligence Newborn – It is 2025 , and I am Here!will see that AI seems to be progressing even faster than in my fiction novel. Perhaps the title should have been “It is 2020, and I am Here!”

Update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that don’t believe it! The current S&P is 1880, flat since EW’s prediction early this year.

It has been a while since I have mentioned the breakthrough computer chip IBM’s TrueNorth. But it is alive and well. There is a start up using this chip on a new designed “Pattern Computer.” This is a desktop supercomputer. It “is highly efficient, extensible, scalable, and unbelievably fast.” It is designed to discover patterns in big data – “where we might otherwise not see them.” “It could be in physics, in climate change, or in anything.” “This could be the advent of an entirely new computer age, and a revolutionary change in human and computer interaction.”

I have said all along that I believe that the real breakout of AI will come from people working to beat the stock market. Following is a site that, to a very small degree, is trying to do that. Their self-learning algorithms are still too narrow in scope, and 15 years of data is not enough. But they have the right idea. The people that are far ahead are not publishing their algorithms.

The U.S. Proposes Spending $4 Billion to Encourage Driverless Cars. The Obama administration aims to remove hurdles to making autonomous cars more widespread. Tesla’s head Elon Musk says that autonomous cars are ahead of schedule, and self-driving cars could be here in as little as a year. Estimates of 25,000 lives being saved once autonomous cars are fully implemented are “driving” this extensive technological effort which includes the development of self-learning algorithms, which are keys to AI.

Watson has been busy since winning on Jeopardy! Watson is now working in 17 different industries. The main industry has been healthcare, and Watson is working with many of the top hospitals and healthcare providers in the world. The mass amount of data alone requires computer help. “In 2015 alone, we will produce something around seven hundred thousand new reference documents in medicine. I am pretty sure most doctors do not have time to read all of them.” Many other professions like law have the same issue of too much data for human comprehension.

“Watson mirrors the human chain of events when we make decisions: it observes, it interprets, it evaluates, and it makes decisions.” “The key breakthrough was that we were able to feed the system natural language text: reference material, the internet, Wikipedia, web pages.” “The basis of the system is that you read it, you train it, and then you continue to learn through use– much like we do. It is mirroring the human learning process.”

Watson Analytics, Watson Discovery Advisor, and Watson Explorer are three service offerings. Watson Analytics learns over time how to identify the correct data sources that you want to apply to a problem and how to make recommendations on how to improve the data to get a better outcome. Watson Discovery Advisor creates a framework for how you can interact with the computer, whether it is asking questions or having a dialogue back and forth. Watson Explorer helps us work within an enterprise to pull together lots of different types of information from existing enterprise data sources, but then take that information and connect it up to cloud-based Watson systems.

“Not only does Watson have the ability to read information, we have both voice-to-text, text-to-voice capabilities, and computer vision capabilities added in. We started teaching Watson to speak new languages. We started with Spanish and Japanese, we have added Brazilian Portuguese; we are working on Arabic right now. French and Italian are right behind it, and German will be in the wings.”

“You see…the third technological revolution getting started here…the information revolution. This is something that is going to be with us for decades.” In my opinion, you are seeing the start of the AI revolution!

Update on EW’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I don’t believe it! The current S&P is 1881, up 0.05%.

What is Elon Musk, the CEO of Tesla and Space X, up to? Many months ago he invested in a company called DeepMind, with the intent of monitoring the advance of AI; to make sure it didn’t go in evil directions that could hurt mankind. Then Google bought DeepMind. Now Elon and several others have “financed” over a billion dollars in a non-profit called OpenAI. It will be co-chaired by Elon and Sam Altman, the CEO of Y Combinator. They already have eight skilled researchers and are in the process of hiring more.

Per the company’s name, they plan to make everything they do public and open-sourced. They are not just going to be monitoring AI’s progress; they will be pushing the AI envelope themselves. Their goal is that once true AI happens, it will not only be in the hands of a few who could use it for evil purposes. Similar to the NRA saying that only a good guy with a gun can stop a bad guy with a gun, OpenAI hopes to have many good guys with thinking AI computers that can minimize or eliminate the power of an evil person, country, or company with AI that is potentially smarter than a million people. Amazon Web Services is donating much computer power and infrastructure. Elon has stated that much information will be shared from Tesla and Space X.

It is my guess that even Elon has been surprised at the progress of self-driving cars and the intelligence of the self-learning systems Tesla has developed. In fact Tesla has recently advertised for more programmers to work on these systems, and Elon has stated that he will interview the candidates himself and the group will be answering directly to him.

I am surprised that I am not getting more comments on this blog. Am I the only one that believes that we may be seeing disruptive gains in AI that exceed the most ambitious timelines “experts” have projected?

I like to read recent books related to AI. “Artificial Intelligence: The God Killer,” by Zed Marston was published on October 3, 2015, and despite it being a short book, it has a 5 star rating on Amazon. It is an easy read.

I believe that this this book, as many books on AI, makes a serious mistake in that it assumes that once computers have abilities exceeding that of humans, they will get almost god-like knowledge and wisdom. But how will this happen? The computers will have to build on the same knowledge base that humans use. They will just do it faster and with more accuracy. No unique knowledge on questions such as “why are we here?” or “why is there anything?” or even “does the theory of evolution really explain everything?” will suddenly appear for the computers! The computers will do a better job of defining the inconsistencies in the traditional religious texts, but people already ignore those inconsistencies. Sure, AI may help us build better telescopes or advanced rocketry for space exploration; but until that data are available it is unlikely that we are going to make breakthroughs or have a better understanding of basic philosophical questions that often drive people to religion or a belief in some creator. Until humans (and maybe even computers) have a better explanation for their existence and the meaning of life, the need for humans to believe in an overall creator will likely continue. In fact, AI may even increase this belief mode because few people will want to accept computers as superior in any way. They will continue to believe that man was created in God’s image, and that we are special.

I have read the Old Testament, the New Testament, and the Qur’an. My personal beliefs are that each is flawed with huge inconsistencies and biases. But others have read one or more of these books and become (or remained) believers. It isn’t that the readers don’t see the inconsistencies. It just is that in most cases they give these religious sources huge latitude in that the books were written by people, not their God, and often written many years after the events described. These believers also don’t see any better explanations. Would not a computer give religion the same latitude, especially given that there seems to be no real explanation of the existence of anything that can be scientifically tested and explained with real confidence? Again, computers may not recognize religion as a strong explanation for anything; but they are unlikely to rule out all religious beliefs without having alternative testable explanations for the basic existence questions.

In my book “Artificial Intelligence Newborn – It is 2025, and I Am Here,” I include the effect of religion. Although the book is fiction, it is an attempt to present a very viable possible future once computers get the ability to think.